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ML
2002
ACM
178views Machine Learning» more  ML 2002»
15 years 6 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
ADAEUROPE
2010
Springer
15 years 4 months ago
Towards the Definition of a Pattern Sequence for Real-Time Applications Using a Model-Driven Engineering Approach
Real-Time (RT) systems exhibit specific characteristics that make them particularly sensitive to architectural decissions. Design patterns help integrating the desired timing behav...
Juan A. Pastor, Diego Alonso, Pedro Sánchez...
JMLR
2010
172views more  JMLR 2010»
15 years 1 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
ACL
2012
13 years 9 months ago
Discriminative Pronunciation Modeling: A Large-Margin, Feature-Rich Approach
We address the problem of learning the mapping between words and their possible pronunciations in terms of sub-word units. Most previous approaches have involved generative modeli...
Hao Tang, Joseph Keshet, Karen Livescu
CVPR
2008
IEEE
16 years 8 months ago
Decomposition, discovery and detection of visual categories using topic models
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and...
Mario Fritz, Bernt Schiele